System, Method and Computer Program Product For Wellbore Event Modeling Using Rimlier Data

ABSTRACT

A data mining and analysis system which analyzes clusters of outlier data (i.e., rimliers) to detect and/or predict downhole events.

FIELD OF THE INVENTION

The present invention relates generally to data mining and analysis and,more specifically, to a system which analyzes one or more clusters ofoutlier wellbore data, or “rimliers,” to model downhole events.

BACKGROUND

In the past, data mining has been proposed to predict wellbore events.Traditionally, after data extraction, the outlier data is removed tomake the data homogeneous because, in order to perform the computationsnecessary to model the data, the system implicitly assumes that the datais homogeneous and of good quality. Thus, if the outlier data were notremoved, conventional time series models, such as Arima, Support VectorMachine, etc., would fail in the drilling domain since they cannotprocess the outlier data, which can be considered as undesirable noisethat would deviate statistical results. Once the outlier data has beenremoved, the cleaned dataset is then utilized to predict events based ona pattern or trend.

However, the traditional method has disadvantages. Primarily, theremoved outlier data may give more insight into past, present or futuredownhole events such as, for example, bit failure, tool failure due tovibration, etc. Instead of representing noise, the removed outlier datamay actually be representative of micro-events of lesser frequency. Assuch, by removing the outlier data, critical data giving insight intodownhole events may be overlooked.

Accordingly, there is a need in the art for system which utilizes theoutlier data to detect and predict wellbore events, thereby harnessesall data available downhole data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a rimlier data analysis systemaccording to an exemplary embodiment of the present invention;

FIG. 2A is a flow chart of a method performed by a rimlier data analysissystem according to an exemplary methodology of the present invention;

FIG. 2B illustrates an exemplary low density rimlier plotted along atime sequence;

FIG. 2C illustrates an exemplary high density rimlier plotted along atime sequence;

FIG. 2D illustrates a data distribution of normal data, low densityoutliers and rimliers according to an exemplary embodiment of thepresent invention;

FIG. 2E illustrates a data distribution of normal and outlier highdensity rimliers according to an exemplary embodiment of the presentinvention;

FIG. 2F illustrates a head-rimlier-tail distribution plotted along atime sequence according to an exemplary embodiment of the presentinvention; and

FIG. 3 illustrates measured while drilling variables and their influencewith respect to time in accordance with an exemplary embodiment of thepresent invention.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Illustrative embodiments and related methodologies of the presentinvention are described below as they might be employed in a system tomodel downhole events using rimlier data. As used herein, “modeling” thedownhole events refers to detecting and/or predicting the downholeevents. In the interest of clarity, not all features of an actualimplementation or methodology are described in this specification. Itwill of course be appreciated that in the development of any such actualembodiment, numerous implementation-specific decisions must be made toachieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which will vary fromone implementation to another. Moreover, it will be appreciated thatsuch a development effort might be complex and time-consuming, but wouldnevertheless be a routine undertaking for those of ordinary skill in theart having the benefit of this disclosure. Further aspects andadvantages of the various embodiments and related methodologies of theinvention will become apparent from consideration of the followingdescription and drawings.

FIG. 1 shows a block diagram of rimlier data analysis system 100according to an exemplary embodiment of the present invention. As willbe described herein, rimlier data analysis system 100 analyzes a group,also referred to herein as cluster, of outlier data showing abnormalbehavior, referred to herein as “rimliers.” Once identified, rimlierdata analysis system 100 analyzes the rimliers to determine those datavariables within the rimliers that indicate the occurrence of a downholeevent. Then, based upon this analysis, rimlier data analysis system 100models (i.e., detect and/or predict) downhole events such as, forexample, those events typically characterized by transient erraticbehavior such as ones caused by tool vibrations, the failure of bearingsin the case of roller cone bits, bit or hole opener teeth failure,increased cuttings bed, whirling of bottomhole assembly, etc.

The data analyzed by rimlier data analysis system 100 may be real-timedata or stored in a local/remote database. The database may include, forexample, general well and job information, job level summary data,pumping schedule individual stage data, or other data typically capturedin daily operations reports to indicate operational progress and theoverall state of the well. Such data may include, for example, finalcasing string components and its set depth, ongoing drill string,bottomhole drilling assembly and drill bit used to drill the hole andits size, etc. Exemplary embodiments of the present invention access thedatabase to extract one or more desired datasets. The system thenanalyzes the dataset for variables indicating patterns or trends and,thus, determines the normal data points and those that deviate from thenormal data points, also known as outliers.

Thereafter, rimlier data analysis system 100 groups the outliers, usingknown statistical mining techniques, and segregates them into lowdensity outlier clusters and high density outlier clusters. As usedherein, clustering refers not only to traditional clustering techniquessuch as, for example, Kernel K-means clustering, but also to othergrouping techniques such as, for example, manual visual identificationand more advanced computational techniques, as will be understood bythose ordinarily skilled in the art having the benefit of thisdisclosure. Low density outlier clusters are those clusters having a lownumber of data points, while high density outlier clusters are thosewhich have a higher number of data points. Those ordinarily skilled inthe art having the benefit of this disclosure realize that thedetermination of which clusters are considered high and low density iscontingent on the total number of data points in a given outlierdataset. For example, in some instances, a 100 data point outliercluster may not reflect an actual downhole problem; but, may insteadreflect an electrical signal spike. In another example, a 10 data pointoutlier cluster may reflect an actual downhole issue and, thus, beconsidered a high density cluster. Nevertheless, as will be describedherein, rimlier data analysis system 100 then analyzes the high densityoutlier cluster, or rimlier, to model downhole events.

Referring to FIG. 1, rimlier data analysis system 100 includes at leastone processor 102, a non-transitory, computer-readable storage 104,transceiver/network communication module 105, optional I/O devices 106,and an optional display 108 (e.g., user interface), all interconnectedvia a system bus 109. Software instructions executable by the processor102 for implementing software instructions stored within rimlieranalysis engine 110 in accordance with the exemplary embodimentsdescribed herein, may be stored in storage 104 or some othercomputer-readable medium.

Although not explicitly shown in FIG. 1, it will be recognized thatrimlier data analysis system 100 may be connected to one or more publicand/or private networks via one or more appropriate network connections.It will also be recognized that the software instructions comprisingrimlier analysis engine 110 may also be loaded into storage 104 from aCD-ROM or other appropriate storage media via wired or wirelesscommunication methods.

Moreover, those skilled in the art will appreciate that the presentinvention may be practiced with a variety of computer-systemconfigurations, including hand-held devices, multiprocessor systems,microprocessor-based or programmable-consumer electronics,minicomputers, mainframe computers, and the like. Any number ofcomputer-systems and computer networks are acceptable for use with thepresent invention. The invention may be practiced indistributed-computing environments where tasks are performed byremote-processing devices that are linked through a communicationsnetwork. In a distributed-computing environment, program modules may belocated in both local and remote computer-storage media including memorystorage devices. The present invention may therefore, be implemented inconnection with various hardware, software or a combination thereof in acomputer system or other processing system.

Still referring to FIG. 1, in certain exemplary embodiments, rimlieranalysis engine 110 comprises data mining module 112 and data analysismodule 114. Rimlier analysis engine 110 provides a technical workflowplatform that integrates various system components such that the outputof one component becomes the input for the next component. In anexemplary embodiment, data mining and analysis engine 110 may be, forexample, the AssetConnet™ software platform commercially availablethrough Halliburton Energy Services Inc. of Houston, Tex. As understoodby those ordinarily skilled in the art having the benefit of thisdisclosure, database mining and analysis engine 110 provides anintegrated, multi-user production engineering environment to facilitatestreamlined workflow practices, sound engineering and rapiddecision-making. In doing so, rimlier analysis engine 110 simplifies thecreation of multi-domain workflows and allows integration of any varietyof technical applications into a single workflow. Those same ordinarilyskilled persons will also realize that other similar workflow platformsmay be utilized with the present invention.

Serving as the database component of rimlier analysis engine 110, datamining module 112 is utilized by processor 102 to capture well relateddatasets for computation from a server database (not shown) or fromreal-time downhole data. In certain exemplary embodiments, the serverdatabase may be, for example, a local or remote SQL server whichincludes data variables related to well job details, wellbore geometrydata, pumping schedule data per stage, post job summaries, bottom-holeinformation, etc. In another exemplary embodiment, data mining module112 receives real-time data from downhole sources using methodologiesknown in the art. As will be described herein, exemplary embodiments ofthe present invention utilize data mining module 112 to capture keyvariables from the database or downhole data source corresponding todifferent job IDs using server queries. After the data is extracted orreceived, rimlier analysis engine 110 communicates the dataset to dataanalysis module 114.

Data analysis module 114 is utilized by processor 102 to analyze thedata extracted by data mining module 112. An exemplary data analysisplatform may be, for example, Matlab®, as will be readily understood bythose ordinarily skilled in the art having the benefit of thisdisclosure. As described herein, rimlier data analysis system 100, viadata analysis module 114, analyzes the dataset to identify rimliers thatare used to model downhole events.

Now referring to FIG. 2A, an exemplary methodology 200 performed by thepresent invention will now be described. In this exemplary methodology,rimlier data analysis system 100 analyzes one or more clusters ofoutlier data, or rimliers, to identify those data variables thatindicate one or more downhole events and, thereafter, models thosedownhole events. For example, rimlier data analysis system 100 may beutilized to detect and/or predict if a particular job has or willexperience a screen-out, damaging vibration event, bit failure, etc. Assuch, the following methodology will describe how rimlier data analysissystem 100 mines and analyzes the data to model such downhole events.

At block 202, rimlier data analysis system 100 initializes and displaysa graphic user interface via display 108, the creation of which will bereadily understood by ordinarily skilled persons having the benefit ofthis disclosure. Here, rimlier data analysis system 100 awaits entry ofqueries reflecting dataset extraction. In one exemplary embodiment, SQLqueries may be utilized to specify the data to be extracted from thedatabase, while log-extract queries are utilized to upload data fromreal-time sources. Such queries may include, for example, fieldlocation, reservoir name, name of the variables, further calculationsrequired for new variables, etc. At block 204, rimlier data analysissystem 100 detects the queries and, at block 206, processor 102instructs data mining module 112 to extract the corresponding dataset(s)from the database or real-time source. Exemplary dataset variables mayinclude, for example, data points related to weights, pressures,temperatures, vertical or rotary speed, slurry volume, proppant mass,etc., for a particular well. In exemplary embodiments, the signal noisemay be eliminated when dual sensors are present that complement thedata, as would be understood by those ordinarily skilled in the arthaving the benefit of this disclosure.

At block 208, rimlier analysis engine 110 analyzes the extracted datasetto determine the outliers. To do so, rimlier analysis engine 110 willanalyze the data based upon a given threshold. In certain exemplaryembodiments, variables may be assigned outlier status if they arecharacterized as values greater than three times the standard deviation,although other merit factors may be utilized. Those variables within thethreshold are considered normal, while those data points outside thethreshold are considered to be outliers. For example, if the extracteddataset related to downhole pressures, those pressures within a certainrange would be considered normal, while those outside that range wouldbe considered as outliers. Once the outliers are determined, rimlieranalysis engine 110 then groups the outliers using a clusteringtechnique such as, for example, Kernel K-means clustering. However,other clustering techniques may be utilized as would be understood bythose ordinarily skilled in the art having the benefit of thisdisclosure.

In certain exemplary embodiments, rimlier analysis engine 110 maypreprocess the extracted data before determining the outliers in orderto remove corrupted data. At times, the data entered into the databasemay comprise incomplete or inconsistent data. Incomplete data mayinclude NAN or NULL data, or data suffering from thoughtless entry.Noisy data may include data resulting from faulty collection or humanerror. Inconsistent data may include data having different formats orinconsistent names.

At block 210, rimlier analysis engine 110 analyzes the clusters todetermine whether there are any high density clusters. As previouslydescribed, those ordinarily skilled in the art having the benefit ofthis disclosure will realize that the determination of which clustersare considered high and low density is contingent on the total number ofdata variables in a given outlier dataset. For example, 2 data pointsmay be considered high density for an outlier cluster having 10 totalvariables, while 200 variables may be considered low density for aoutlier cluster having 1000 variables. Therefore, certain exemplaryembodiments of rimlier analysis engine 110 may make this determination,for example, based upon a pre-defined threshold or a threshold entereddynamically via the user interface.

If, at block 210, rimlier analysis engine 110 logically determines a“No,” the algorithm loops back to block 204 and begins again. If,however, rimlier analysis engine 110 determines a “Yes” (i.e., highdensity outlier clusters exist), these high density clusters will beflagged as rimliers at block 212. To illustrate this point, FIG. 2Bshows an exemplary time sequence distribution T₀ . . . T_(n) of a lowdensity cluster having only a few data spikes (outliers) correspondingto one or more real-time downhole assembly measurements D₀ . . . D_(n)(stand-pipe pressure, torque, weight on bit, bit rotation speed, etc.,for example), while FIG. 2C shows a similar distribution of a highdensity cluster having multiple data spikes (outliers), as opposed tonormal data points. FIG. 2D shows an exemplary distribution of low andhigh density outliers along the X,Y planes. Here, normal and outlierdata points have been clustered and plotted by rimlier analysis engine110. It is then found that the extracted dataset contained low densityoutliers 1 and 2 and high density outlier clusters, or rimliers, 1 and2. Accordingly, at block 212, rimlier analysis engine 110 then flagshigh density outliers 1 and 2 as rimliers 1 and 2.

At block 214, rimlier analysis engine 110, using data analysis module114, analyzes the rimliers to identify those variables which can be usedto model downhole events. To accomplish this, rimlier analysis engine110 may utilize a variety of multivariate statistical techniques suchas, for example, least squares regression, neural networks, fuzzy orhybrid neuro-fuzzy, rule-based, case-based or decision tree techniques.As will be understood by those ordinarily skilled in the art having thebenefit of this disclosure, utilizing such techniques, the presentinvention interpolates based upon principles of physics, existingstatistical models, historical data, and recent behavior to determinethe likely consequences or projected future of the well and itscomponent based upon presence of the rimliers. As previously described,presence of the rimliers may indicate, for example, the possibledeterioration of the bit performance leading to insert failure orpossible costly remediation trips.

In a first exemplary methodology, rimlier analysis engine 110 mayperform a micro-analysis of a single rimlier at block 214(a). Here,referring to FIG. 2E, rimlier analysis engine 110 further clusters thesingle rimlier into a normal high density rimlier and outlier highdensity rimlier . Rimlier analysis engine 110 then analyzes the outlierhigh density rimlier to determine if further micro-clustering ispossible, while the normal high density outlier 1 is discarded (since itreally is not a significant rimlier). If a given micro-cluster is faraway from other clusters along the plot, this may indicate there areoutliers within the rimlier. For example, there may be multiple negativerotational speeds which exceed the mechanical threshold for the drillstring. As such, rimlier analysis engine 110 may continue themicro-clustering of subsequent rimliers until those rimliers that arethe specific signatures of a possible undesirable event are isolated andidentified. Thus, this option allows rimlier analysis engine 110 toeliminate unnecessary outliers within the rimlier or to identifyadditional clusters useful in event prediction and detection. Thisalgorithm continues iteratively until, ultimately, at block 216, rimlieranalysis engine 110 models downhole events.

In a second exemplary methodology, rimlier analysis engine 110 mayperform a macro-analysis of multiple rimliers at block 214(b). Amongother things, the macro-analysis can be used to study the pattern of therimliers so that events can be predicted. In addition, rimlier analysisengine 110 may also analyze the rimliers to identify patterns,variances, trends, classes, various responses, etc., as will beunderstood by those ordinarily skilled in the art having the benefit ofthis disclosure. Entropy techniques, as will be understood by thoseordinarily skilled in the art having the benefit of this disclosure, canbe utilized to predict, for example, tool failures, vibration—lateral orradial etc. In addition, rimlier analysis engine 110 may utilize entropyto study the homogeneity of the rimliers, which will ensure the rimliershave uniform data over a given period of time. The entropy ofhomogeneous data is zero, while the entropy of the rimliers must becalculated.

Referring to FIG. 2F, a time series distribution of a head-rimlier-tailis plotted to further illustrate this exemplary methodology. To performthe entropy analysis, rimlier analysis engine 110 must determine therelative entropy between the head data and the rimlier data and the taildata and rimlier data using the following:

Entropy is defined as E=Σ−p(x)log(x)   Eq. (1)

where p(x) is the probability of x.

Here, rimlier analysis engine 110 first utilizes a clustering techniqueto detect and add rimliers, as previously described. In addition toclustering, other techniques may be utilized to detect and add rimlierssuch as, for example, rule-based, density-based, decomposition, SVM,neural network, etc., as will be understood by ordinarily skilledpersons having the benefit of this disclosure. Second, rimlier analysisengine 110 calculates the Head and Tail for the rimlier factors. HeadRimlier Factor is defined as the ratio of the entropy of the head to therimlier data, as shown below:

$\begin{matrix}{{{Head}\mspace{14mu} {Rimlier}\mspace{14mu} {Factor}} = {\frac{E_{h}}{E_{r}} = \frac{\Sigma - {{p_{h}(x)}{\log (x)}}}{\Sigma - {{p_{r}(x)}{\log (x)}}}}} & {{Eq}.\mspace{14mu} (2)}\end{matrix}$

Tail Rimlier Factor is defined as the ratio of the entropy of the tailto the rimlier data, and is calculated by rimlier analysis engine 110 asfollows:

$\begin{matrix}{{{Tail}\mspace{14mu} {Rimlier}\mspace{14mu} {Factor}} = {\frac{Et}{E_{r}} = \frac{\Sigma - {{p_{t}(x)}{\log (x)}}}{\Sigma - {{p_{r}(x)}{\log (x)}}}}} & {{Eq}.\mspace{14mu} (3)}\end{matrix}$

Once rimlier analysis engine 110 calculates the ratios (i.e., the Headand Tail Rimlier Factors), they are then used by rimlier analysis engine110 to quantify and predict, or model, downhole events at block 216. Forexample, an increase in the rimlier ratio or density beyond a definedthreshold and, therefore, an increase in weight and significance,indicates a present or impending downhole event. However, if the rimlierratio is decreasing, the problem is vanishing. In an alternativeexemplary embodiment, rimlier analysis engine 110 may utilize this ratiofor multiple clusters. Again, if the ratio begins to decrease, thisindicates there are no downhole problems. However, if this this ratiostarts increasing, it may result in a catastrophic failure. In suchscenarios, at block 216, rimlier analysis engine 110 may transmit analert signal via the user interface to alert the user based upon apredefined user threshold.

In other exemplary embodiments, rimlier analysis engine 110 may alsocompare this ratio against the mechanical or hydro mechanical specificenergy to determine or predict the downhole problems. By performing thiscomparison, rimlier analysis engine 110 may determine how the energy inthe downhole assembly is expended (i.e., if system efficiency isincreasing or decreasing). For example, a decrease in system efficiencyindicates the presence of present or future downhole event, while anincrease in system efficiency indicates there are no issues.Accordingly, at block 216, such events are modeled by rimlier analysisengine 110, whereby the events are predicted and/or detected.

In certain exemplary embodiments, rimlier analysis engine 110 can alsoutilize entropy to cross-correlate with other data such as, for example,similar tool data at different depths, as well as gamma ray, resistivityand other measurements received from other tools in the drill string.Such data from other tools may be received in real-time or from databasestorage. Through cross-correlation of this data, rimlier analysis engine110 may counter verify, eliminate or substantiate the results. Forexample, the erratic variation of the bit torque may be due to a changein the formation observed through the gamma ray log—not due to a bitteeth problem. In such an embodiment, the rimlier data lies withinmultidimensional space with several variables which are cross-correlatedwith gamma ray and other logs to determine whether certain events aredue to alterable variables (flow rate, for example), which can beeliminated or avoided, or unalterable variables (formation, forexample). Thereafter, at block 216, in addition to predicting and/ordetecting events, rimlier analysis engine 110 may also determine whethercertain events can be avoided.

Accordingly, based on the foregoing analysis, rimlier analysis engine110 models wellbore events. In addition to certain sustained data pointsindicating downhole events, different trends, for example, may be usedto indicate events. For example, analysis of the rimlier data mayindicate that the drag in the string is increasing at the surface;however, the data may also reflect an increasing entropy trendindicating an future stuck pipe event. Similarly, string and bit teethfailure may also be detected, for example.

Rimlier analysis engine 110 may output the results in a variety of wayssuch as, for example, an earth model, plotted graph, two orthree-dimensional image, etc., as would be understood by thoseordinarily skilled in the art having the benefit of this disclosure. Inthis regard, visualization of data is an important feature of any datamining analysis. Once the dimension of the data is 3 or higher, humanvisualization of data becomes quite difficult. As such, certainexemplary embodiments of the present invention utilize MultidimensionalScaling (“MDS”) at block 216 to enhance the analysis of WDMA system 100with data visualization, as this technique reduces the dimension of thedata for visualization purposes, as will be understood by thoseordinarily skilled in the art having the benefit of this disclosure.

Referring to FIG. 3, certain exemplary embodiments of the presentinvention may also utilize different distributions or spectral analysisto analyze the rimliers so that the influencing parameters for a givenevent can be determined. Such distribution analysis can be used to studythe data in the frequency domain and is known in the art. In the exampleshown in FIG. 3, rimliers A, B and C are plotted to represent a drillingmeasured variable D₀ . . . D_(n) and its influence on rimliers A, B, andC with respect to time T₀ . . . T_(n). In the alternative, thedistribution or spectral analysis may be based upon some other variablesuch as, for example, depth. In another embodiment, heat maps (notshown) of variables can also be displayed to indicate danger events andtheir increased presence. In yet another exemplary embodiment, therimlier analysis engine 110 may utilized the predicted failure events todetermine or estimate non-productive time by translating such eventsaccordingly, as would be understood by those ordinarily skilled in theart having the benefit of this disclosure.

In yet another exemplary embodiment, rimlier data analysis system 100may predict a drilling rate or bit life using data from a single well ormultiple wells. Through utilization of one or more of the analysismethods described above, rimlier analysis engine 110 calculatesadjustment factors between the actual and modeled rimlier values ofdrilling related data. Calculation of such adjustment factors may, forexample, be conducted iteratively or algorithmically to match the actualdata, as will be understood by those ordinarily skilled in the arthaving the benefit of this disclosure. For example, a calculated valueof 100 and actual value of 110 has an adjustment factor of 1.1.

Nevertheless, once the adjustment factors are calculated, rimlieranalysis engine 110 may determine the trend and assign a correlatingweighting factor to perform forward modeling. For example, comparison ofspecific energy calculations to calculations of rock strength, eitherunconfined rock strength or confined rock strength enables continuousevaluation of drilling performance to identify limiters such as, forexample, flounder points, in the drilling system, teeth wear or bit nearfailure. Here, based upon the comparison, rimlier analysis engine 110determines the energy necessary to supply to the bit in order tobreakdown the formation, thus ensuring bit life is used effectively.

Accordingly, in certain exemplary embodiments, rimlier analysis engine110 may recommend drilling parameters to ensure optimal drillingefficiency and bit life. As downhole formation evaluation tools updateand correct for formation variations that result in varying formationcompressive strengths, rimlier analysis engine 110 may recalculate thebit wear and life variations accordingly. In such embodiments, rimlierdata analysis system 100 receives real-time data from downhole sensors,as would be understood by those ordinarily skilled in the art having thebenefit of this disclosure.

As described herein, exemplary embodiments of the present inventionprovide systems to data-mine and identify rimlier data to detect and/orprevent downhole events, thus providing valuable insight into drillingoperations, production enhancement and well stimulation/completion.Since certain exemplary embodiments of the present invention onlyanalyze the rimliers, a fast and efficient statistical process isprovided which requires less storage space and processing power thanprior art systems.

Moreover, the ability of the present invention to cluster downhole datacoupled with analysis of only the rimliers will provide added insightsinto real-time or predicted events. As described herein, clustering highdensity rimlier data will enable detecting and/or prediction of eventssuch as, for example, bit failure, tool failure due to vibration, etc.In addition, the present invention also determines whether certainpredicted or detected events are alterable or unalterable. Furthermore,the present invention is also useful in its ability to presents theresults in a simple, intuitive and easy to understand format that makesit a very efficient tool to predict and/or detect downhole events.

The foregoing methods and systems described herein are particularlyuseful in planning, altering and/or drilling wellbores. As described,the system analyses one or more rimliers to identify characteristicsthat may be used to predict and/or detect well events. Once identified,the detected/predicted events may then be presented visually via theuser interface. This data can then be utilized to identify wellequipment and develop a well workflow or stimulation plan. Thereafter,the wellbore is drilled, stimulated, altered and/or completed inaccordance to those characteristics identified using the presentinvention.

Those of ordinary skill in the art will appreciate that the methods ofthe present invention may also be implemented dynamically. Thus, a wellplacement or stimulation plan may be updated in real-time based upon theoutput of the present invention. Also, after implementing the wellplacement or stimulation plan, the system of the invention may beutilized during the completion process on the fly or iteratively todetermine optimal well trajectories, fracture initiation points and/orstimulation design as wellbore parameters change or are clarified oradjusted. In either case, the results of the dynamic calculations may beutilized to alter a previously implemented well placement or stimulationplan.

An exemplary methodology of the present invention provides acomputer-implemented method to model downhole events, the methodcomprising extracting a dataset from a database, the dataset comprisingnormal wellbore data and outlier wellbore data, clustering a pluralityof the outlier data to form a rimlier, analyzing the rimlier todetermine those data variables within the rimlier that indicate adownhole event, and modeling the downhole event based upon the analysisof the rimlier. In another method, clustering the plurality of outlierdata to form a rimlier further comprises clustering the plurality ofoutlier data into a plurality of clusters, segregating the plurality ofclusters into a high density cluster and a low density cluster, andflagging those high density clusters as the rimlier. Yet another methodfurther comprises removing corrupted data from the extracted dataset.

In another method, analyzing the rimlier further comprises segregatingthe rimlier into a normal high density rimlier and a outlier highdensity rimlier and analyzing the outlier high density rimlier todetermine those data variables that indicate the downhole event. In yetanother, clustering the plurality of the outlier data to form therimlier further comprises forming a plurality of rimliers. In another,modeling the downhole event further comprises modeling an energyefficiency of a downhole assembly. Yet another method further comprisesdetermining whether the modeled downhole event can be avoided. Anothermethod further comprises producing an alert signal corresponding to themodeled downhole event. Yet another method further comprises displayingthe modeled downhole event in the form of a tree or earth model. In yetanother, a wellbore is drilled, completed or stimulated in accordance tothe modeled downhole events.

Another exemplary embodiment of the present invention provides a systemcomprising processing circuitry to perform the methods described herein.Yet another exemplary embodiment of the present invention provides acomputer program product comprising instructions which, when executed byat least one processor, causes the processor to perform the methodsdescribed herein.

Although various embodiments and methodologies have been shown anddescribed, the invention is not limited to such embodiments andmethodologies and will be understood to include all modifications andvariations as would be apparent to one skilled in the art. For example,although described herein as utilizing rimlier data, exemplaryembodiments of the present invention may also use normal data inconjunction with rimliers to detect or model downhole events. Therefore,it should be understood that the invention is not intended to be limitedto the particular forms disclosed. Rather, the intention is to cover allmodifications, equivalents and alternatives falling within the spiritand scope of the invention as defined by the appended claims.

1. A computer-implemented method to model downhole events, the methodcomprising: extracting a dataset from a database, the dataset comprisingnormal wellbore data and outlier wellbore data; clustering a pluralityof the outlier data into a plurality of clusters; segregating theplurality of clusters into a high density cluster and a low densitycluster, wherein the high density clusters are utilized as a rimlier;analyzing the rimlier to determine those data variables within therimlier that indicate a downhole event; and modeling the downhole eventbased upon the analysis of the rimlier.
 2. A computer-implemented methodas defined in claim 1, wherein analyzing the rimlier further comprises:determining a Head Rimlier Factor as defined by:${\frac{E_{h}}{E_{r}} = \frac{\Sigma - {{p_{h}(x)}{\log (x)}}}{\Sigma - {{p_{r}(x)}{\log (x)}}}};$and determining a Tail Rimlier Factor as defined by:${\frac{Et}{E_{r}} = \frac{\Sigma - {{p_{t}(x)}{\log (x)}}}{\Sigma - {{p_{r}(x)}{\log (x)}}}},$wherein the Head Rimlier Factor and the Tail Rimlier Factor are utilizedto determine those data variables indicating the downhole event.
 3. Acomputer-implemented method as defined in claim 1, further comprisingremoving corrupted data from the extracted dataset.
 4. Acomputer-implemented method as defined in claim 1, wherein analyzing therimlier further comprises: segregating the rimlier into a normal highdensity rimlier and a outlier high density rimlier; and analyzing theoutlier high density rimlier to determine those data variables thatindicate the downhole event.
 5. A computer-implemented method as definedin claim 1, wherein clustering the plurality of the outlier data furthercomprises forming a plurality of rimliers.
 6. A computer-implementedmethod as defined in claim 5, wherein modeling the downhole eventfurther comprises modeling an energy efficiency of a downhole assembly.7. A computer-implemented method as defined in claim 1, furthercomprising determining whether the modeled downhole event can beavoided.
 8. A computer-implemented method as defined in claim 1, furthercomprising producing an alert signal corresponding to the modeleddownhole event.
 9. A computer-implemented method as defined in claim 1,further comprising displaying the modeled downhole event in the form ofa tree or earth model.
 10. A computer-implemented method as defined inclaim 1, wherein a wellbore is drilled, completed or stimulated inaccordance to the modeled downhole events. 11-30. (canceled)
 31. Acomputer-implemented method as defined in claim 5, wherein analyzing theplurality of rimliers further comprises determining a pattern across theplurality of rimliers, wherein the downhole events are modeled basedupon the determined patterns.
 32. A system comprising processingcircuitry to perform the method of claim
 1. 33. A computer programproduct comprising instructions which, when executed by at least oneprocessor, causes the processor to perform the method of claim 1.